2025 Theses Doctoral

Understanding Regional Aerosol Variability and Trends Through Satellite Constraints on Source Region Drivers and Arctic Processes

Smith, Sarah Elise

Globally, aerosols exert a strong influence on Earth’s energy balance by absorbing or reflecting solar radiation, and modifying cloud radiative characteristics. However, both global and regional radiative effects from aerosol remain highly uncertain, both in the present climate and under future emissions scenarios. In the present climate, some of that uncertainty arises from poor constraints on the variability of aerosol loadings in space and time.

This is especially true in remote regions like the Arctic, where observations are sparse. As aerosol radiative effects depend on factors such as solar insolation, temperature, surface albedo, and moisture availability---all of which also vary across regions and seasons---improving constraints on the spatiotemporal variability of aerosol within remote regions, and the processes governing that variability, is an important step for understanding the energy budget in such locations.

The Arctic in particular is both highly sensitive to variations in radiative forcing, and also shapes important feedbacks that affect the rest of the global climate. Hence, constraints on the processes governing aerosol variability in this region are especially important for understanding both the regional energy balance and the long term effects of Arctic warming on the broader climate system. In populated, industrial regions, observations are less limited, but uncertainty surrounding future aerosol impacts arises from challenges disentangling natural and anthropogenic signals, as well as scenario uncertainty stemming from the inherent unpredictability of human activities. Recent air pollution regulations in many countries have produced multi-year declines in anthropogenic emissions.

At the same time, increasing summertime emissions from wildfires have changed the chemical and seasonal distribution of global and regional aerosol burdens. In the near-future, anticipated further declines in industrial emissions are expected to unmask additional warming from greenhouse gases. However, such declines are likely to be regionally inhomogenous, and the extent to which political and social changes influence local emissions remains difficult to predict. Lockdown periods during the COVID-19 pandemic provided an opportunity to examine the effects of lifestyle changes on aerosol burdens, globally and in different source regions, and to disentangle the effects of societal changes from long-term trends and natural sources of variability.

Satellite observations of aerosol optical depth (AOD) are widely used for assessing variability and trends in global and regional aerosol burdens, providing high resolution, long-term coverage across much of the globe. Indeed, these data products play a central role in this dissertation. However, over the course of my research I found that many satellite and reanalysis AOD products exhibited a seasonal cycle opposite to that found in ground-based station measures and satellite lidar products, over the mid to high latitudes. This discrepancy suggests that biases in retrieval processing may be distorting representations of seasonality in these regions. Understanding the causes of these biases and assessing the magnitude of their effects is an important step toward improving aerosol characterization in future research, while also offering an opportunity to address fundamental questions in remote sensing.

In Chapters 2 and 3 of this dissertation, I use satellite observations of aerosol optical depth (AOD) to examine the processes governing aerosol spatiotemporal variability in the Arctic, and to disentangle the effects of long-term trends, natural variability, and pandemic-related lifestyle changes on source region aerosol burdens over the first year of the pandemic. In Chapter 4, I assess the extent to which sampling biases, data quality, retrieval geometry, and lidar retrieval artifacts contribute to the biases in seasonality described above.

To address the first question, I applied a K-Means clustering algorithm to monthly median (2007-2021) Arctic AODs, finding four distinct aerosol seasonality regimes. The subregions corresponding to each regime exhibited distinct topographic, ecological, and meteorological characteristics that likely affect transport, emissions, and deposition. This chapter constrains aerosol variability in the Arctic and identifies potential drivers of subregional variations in seasonality.To understand the role of lifestyle changes on regional aerosol burdens, I compared the effects of the pandemic lockdowns with long-term trends and variations in AOD from natural aerosol sources, such as dust and smoke, in four major Northern Hemisphere source regions.

I found that in most regions, the lockdown-signature was smaller than the effects of long-term trends. In one region (India), natural variability from dust emissions eclipsed the lockdown-signature by an order of magnitude, while smoke emissions from wildfires in the Western United States (US) masked pandemic-signatures in both the US and Europe in the later part of the year. These findings suggest that emissions changes resulting from individual lifestyle changes may play a relatively minor role in shaping global and regional aerosol burdens, and that policy-driven changes, climate change feedbacks, and natural variability in aerosol emissions may exert greater influence on future aerosol trends and their associated climate impacts.

Finally, in Chapter 4 I use colocated retrievals from lidar and a passive sensor instrument to show that seasonality biases between data products arise from the interplay between passive sensor retrieval quality and retrieval geometry. Specifically, passive sensor retrievals flagged as "low-quality" declined relative to lidar measures as the solar zenith angle (SZA) increased, while those flagged as "high-quality" remained stable. In the winter in the mid to high latitudes, "low-quality" retrievals predominate and the SZA is high, resulting in systematically lower average passive sensor AODs. In contrast, the NH summer is characterized primarily by “high-quality” retrievals at most latitudes, such that aggregate products maintain a constant high bias relative to lidar.

I further demonstrate that the drivers of globally low biases in lidar products do not vary with solar geometry, and that the effects of sampling biases on seasonality are small compared to the combined influence of retrieval quality and the SZA on passive sensor retrievals. The findings described in Chapter 4 will help data users interpret measurements of Arctic and midlatitude aerosol in different seasons, while also contributing to improved constraints on satellite retrievals of reflectance and AOD under complex viewing conditions.